Overview

Dataset statistics

Number of variables8
Number of observations392
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.6 KiB
Average record size in memory72.0 B

Variable types

Numeric8

Alerts

age is highly overall correlated with pregHigh correlation
insu is highly overall correlated with plasHigh correlation
mass is highly overall correlated with skinHigh correlation
plas is highly overall correlated with insuHigh correlation
preg is highly overall correlated with ageHigh correlation
skin is highly overall correlated with massHigh correlation
preg has 56 (14.3%) zerosZeros

Reproduction

Analysis started2024-03-30 00:18:08.155615
Analysis finished2024-03-30 00:18:12.799168
Duration4.64 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

preg
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3010204
Minimum0
Maximum17
Zeros56
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-03-30T01:18:12.850179image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2114245
Coefficient of variation (CV)0.9728581
Kurtosis1.4863417
Mean3.3010204
Median Absolute Deviation (MAD)1
Skewness1.3355963
Sum1294
Variance10.313247
MonotonicityNot monotonic
2024-03-30T01:18:12.939200image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 93
23.7%
2 64
16.3%
0 56
14.3%
3 45
11.5%
4 27
 
6.9%
5 21
 
5.4%
7 20
 
5.1%
6 19
 
4.8%
8 14
 
3.6%
9 11
 
2.8%
Other values (7) 22
 
5.6%
ValueCountFrequency (%)
0 56
14.3%
1 93
23.7%
2 64
16.3%
3 45
11.5%
4 27
 
6.9%
5 21
 
5.4%
6 19
 
4.8%
7 20
 
5.1%
8 14
 
3.6%
9 11
 
2.8%
ValueCountFrequency (%)
17 1
 
0.3%
15 1
 
0.3%
14 1
 
0.3%
13 3
 
0.8%
12 5
 
1.3%
11 5
 
1.3%
10 6
 
1.5%
9 11
2.8%
8 14
3.6%
7 20
5.1%

plas
Real number (ℝ)

HIGH CORRELATION 

Distinct117
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.62755
Minimum56
Maximum198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-03-30T01:18:13.042222image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile81
Q199
median119
Q3143
95-th percentile181
Maximum198
Range142
Interquartile range (IQR)44

Descriptive statistics

Standard deviation30.860781
Coefficient of variation (CV)0.2516627
Kurtosis-0.48322696
Mean122.62755
Median Absolute Deviation (MAD)21
Skewness0.51784994
Sum48070
Variance952.38778
MonotonicityNot monotonic
2024-03-30T01:18:13.260273image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 14
 
3.6%
99 10
 
2.6%
129 9
 
2.3%
95 8
 
2.0%
88 8
 
2.0%
126 7
 
1.8%
117 7
 
1.8%
128 7
 
1.8%
109 7
 
1.8%
112 6
 
1.5%
Other values (107) 309
78.8%
ValueCountFrequency (%)
56 1
 
0.3%
68 3
0.8%
71 2
 
0.5%
74 3
0.8%
75 1
 
0.3%
77 2
 
0.5%
78 2
 
0.5%
79 2
 
0.5%
80 2
 
0.5%
81 5
1.3%
ValueCountFrequency (%)
198 1
 
0.3%
197 2
0.5%
196 2
0.5%
195 1
 
0.3%
193 1
 
0.3%
191 1
 
0.3%
189 2
0.5%
188 1
 
0.3%
187 4
1.0%
186 1
 
0.3%

pres
Real number (ℝ)

Distinct37
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.663265
Minimum24
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-03-30T01:18:13.370297image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile50
Q162
median70
Q378
95-th percentile90
Maximum110
Range86
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.496092
Coefficient of variation (CV)0.17684
Kurtosis0.79540444
Mean70.663265
Median Absolute Deviation (MAD)8
Skewness-0.087516392
Sum27700
Variance156.1523
MonotonicityNot monotonic
2024-03-30T01:18:13.464318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
70 31
 
7.9%
74 30
 
7.7%
64 27
 
6.9%
68 24
 
6.1%
72 23
 
5.9%
78 23
 
5.9%
60 20
 
5.1%
76 20
 
5.1%
62 19
 
4.8%
58 18
 
4.6%
Other values (27) 157
40.1%
ValueCountFrequency (%)
24 1
 
0.3%
30 2
 
0.5%
38 1
 
0.3%
40 1
 
0.3%
44 3
 
0.8%
46 2
 
0.5%
48 3
 
0.8%
50 10
2.6%
52 6
1.5%
54 8
2.0%
ValueCountFrequency (%)
110 2
 
0.5%
106 2
 
0.5%
102 1
 
0.3%
100 2
 
0.5%
98 1
 
0.3%
94 2
 
0.5%
92 1
 
0.3%
90 11
2.8%
88 15
3.8%
86 11
2.8%

skin
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.145408
Minimum7
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-03-30T01:18:13.561340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile13
Q121
median29
Q337
95-th percentile46.45
Maximum63
Range56
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.516424
Coefficient of variation (CV)0.3608261
Kurtosis-0.45769609
Mean29.145408
Median Absolute Deviation (MAD)8
Skewness0.20931081
Sum11425
Variance110.59517
MonotonicityNot monotonic
2024-03-30T01:18:13.665568image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
32 20
 
5.1%
30 18
 
4.6%
33 17
 
4.3%
23 16
 
4.1%
18 16
 
4.1%
27 14
 
3.6%
26 14
 
3.6%
29 14
 
3.6%
28 13
 
3.3%
25 12
 
3.1%
Other values (38) 238
60.7%
ValueCountFrequency (%)
7 2
 
0.5%
8 1
 
0.3%
10 3
 
0.8%
11 5
1.3%
12 6
1.5%
13 10
2.6%
14 6
1.5%
15 11
2.8%
16 5
1.3%
17 10
2.6%
ValueCountFrequency (%)
63 1
 
0.3%
60 1
 
0.3%
56 1
 
0.3%
52 2
 
0.5%
51 1
 
0.3%
50 3
0.8%
49 3
0.8%
48 4
1.0%
47 4
1.0%
46 7
1.8%

insu
Real number (ℝ)

HIGH CORRELATION 

Distinct184
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.05612
Minimum14
Maximum846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-03-30T01:18:13.772583image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile42.55
Q176.75
median125.5
Q3190
95-th percentile396.5
Maximum846
Range832
Interquartile range (IQR)113.25

Descriptive statistics

Standard deviation118.84169
Coefficient of variation (CV)0.76153174
Kurtosis6.3565051
Mean156.05612
Median Absolute Deviation (MAD)54.5
Skewness2.1651162
Sum61174
Variance14123.347
MonotonicityNot monotonic
2024-03-30T01:18:13.875606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 11
 
2.8%
130 9
 
2.3%
140 9
 
2.3%
120 8
 
2.0%
94 7
 
1.8%
180 7
 
1.8%
100 7
 
1.8%
135 6
 
1.5%
115 6
 
1.5%
110 6
 
1.5%
Other values (174) 316
80.6%
ValueCountFrequency (%)
14 1
 
0.3%
15 1
 
0.3%
16 1
 
0.3%
18 2
0.5%
22 1
 
0.3%
23 1
 
0.3%
25 1
 
0.3%
29 1
 
0.3%
32 1
 
0.3%
36 3
0.8%
ValueCountFrequency (%)
846 1
0.3%
744 1
0.3%
680 1
0.3%
600 1
0.3%
579 1
0.3%
545 1
0.3%
543 1
0.3%
540 1
0.3%
510 1
0.3%
495 2
0.5%

mass
Real number (ℝ)

HIGH CORRELATION 

Distinct194
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.086224
Minimum18.2
Maximum67.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-03-30T01:18:13.979133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile22.255
Q128.4
median33.2
Q337.1
95-th percentile45.245
Maximum67.1
Range48.9
Interquartile range (IQR)8.7

Descriptive statistics

Standard deviation7.0276592
Coefficient of variation (CV)0.21240439
Kurtosis1.5565131
Mean33.086224
Median Absolute Deviation (MAD)4.5
Skewness0.66348506
Sum12969.8
Variance49.387994
MonotonicityNot monotonic
2024-03-30T01:18:14.086158image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.3 7
 
1.8%
32 7
 
1.8%
31.6 6
 
1.5%
33.6 5
 
1.3%
35.5 5
 
1.3%
25.2 5
 
1.3%
28.7 5
 
1.3%
30.8 5
 
1.3%
33.2 5
 
1.3%
39.4 5
 
1.3%
Other values (184) 337
86.0%
ValueCountFrequency (%)
18.2 1
0.3%
19.3 1
0.3%
19.4 1
0.3%
19.5 2
0.5%
19.6 2
0.5%
20.1 1
0.3%
20.4 2
0.5%
20.8 2
0.5%
21.1 1
0.3%
21.2 1
0.3%
ValueCountFrequency (%)
67.1 1
0.3%
59.4 1
0.3%
57.3 1
0.3%
55 1
0.3%
53.2 1
0.3%
52.3 1
0.3%
49.7 1
0.3%
47.9 1
0.3%
46.8 1
0.3%
46.7 1
0.3%

pedi
Real number (ℝ)

Distinct331
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52304592
Minimum0.085
Maximum2.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-03-30T01:18:14.192182image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.085
5-th percentile0.15355
Q10.26975
median0.4495
Q30.687
95-th percentile1.16035
Maximum2.42
Range2.335
Interquartile range (IQR)0.41725

Descriptive statistics

Standard deviation0.34548804
Coefficient of variation (CV)0.660531
Kurtosis6.3666899
Mean0.52304592
Median Absolute Deviation (MAD)0.192
Skewness1.9591012
Sum205.034
Variance0.11936199
MonotonicityNot monotonic
2024-03-30T01:18:14.302712image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.692 4
 
1.0%
0.452 3
 
0.8%
0.687 3
 
0.8%
0.422 3
 
0.8%
0.299 3
 
0.8%
0.26 3
 
0.8%
0.496 3
 
0.8%
0.261 3
 
0.8%
0.637 2
 
0.5%
0.678 2
 
0.5%
Other values (321) 363
92.6%
ValueCountFrequency (%)
0.085 1
0.3%
0.088 1
0.3%
0.089 1
0.3%
0.101 1
0.3%
0.107 1
0.3%
0.115 1
0.3%
0.118 1
0.3%
0.122 1
0.3%
0.123 1
0.3%
0.127 1
0.3%
ValueCountFrequency (%)
2.42 1
0.3%
2.329 1
0.3%
2.288 1
0.3%
2.137 1
0.3%
1.699 1
0.3%
1.6 1
0.3%
1.4 1
0.3%
1.391 1
0.3%
1.39 1
0.3%
1.353 1
0.3%

age
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.864796
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-03-30T01:18:14.403735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q123
median27
Q336
95-th percentile52.45
Maximum81
Range60
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.200777
Coefficient of variation (CV)0.33049875
Kurtosis1.7375308
Mean30.864796
Median Absolute Deviation (MAD)5
Skewness1.4036065
Sum12099
Variance104.05584
MonotonicityNot monotonic
2024-03-30T01:18:14.509759image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
22 43
 
11.0%
21 33
 
8.4%
24 31
 
7.9%
25 30
 
7.7%
23 28
 
7.1%
26 24
 
6.1%
28 21
 
5.4%
29 14
 
3.6%
27 14
 
3.6%
31 12
 
3.1%
Other values (33) 142
36.2%
ValueCountFrequency (%)
21 33
8.4%
22 43
11.0%
23 28
7.1%
24 31
7.9%
25 30
7.7%
26 24
6.1%
27 14
 
3.6%
28 21
5.4%
29 14
 
3.6%
30 10
 
2.6%
ValueCountFrequency (%)
81 1
 
0.3%
63 1
 
0.3%
61 1
 
0.3%
60 2
0.5%
59 1
 
0.3%
58 4
1.0%
57 2
0.5%
56 1
 
0.3%
55 2
0.5%
54 2
0.5%

Interactions

2024-03-30T01:18:12.075605image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:08.231023image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:08.798350image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:09.355191image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:09.877812image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:10.508459image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:11.028576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:11.545196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:12.150622image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:08.313041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:08.873367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:09.426207image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:09.951829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:10.578475image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:11.100592image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:11.616502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:12.223638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:08.387058image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:08.946594image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:09.497223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:10.022845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:10.648491image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:11.170112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:11.688517image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:12.287653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:08.454577image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:09.011609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:09.557236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:10.085860image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:10.709505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:11.230125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:11.751532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:12.353667image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:08.523592image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:09.080624image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:09.623755image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:10.149874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:10.772518image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:11.293140image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:11.817551image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:12.416682image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:08.590608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:09.145641image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:09.684769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:10.213888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:10.833532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:11.354153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:11.880561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:12.480696image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:08.655622image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:09.211654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:09.745783image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:10.276903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:10.895546image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:11.415167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:11.942575image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:12.547215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:08.723638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:09.282174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:09.811798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:10.341917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:10.962561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:11.478181image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T01:18:12.008590image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-03-30T01:18:14.583775image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ageinsumasspediplaspregpresskin
age1.0000.2610.1670.1030.3500.6340.3290.242
insu0.2611.0000.3010.1320.6590.1230.1320.241
mass0.1670.3011.0000.0960.199-0.0660.3170.674
pedi0.1030.1320.0961.0000.0890.012-0.0210.093
plas0.3500.6590.1990.0891.0000.1900.2370.216
preg0.6340.123-0.0660.0120.1901.0000.1520.055
pres0.3290.1320.317-0.0210.2370.1521.0000.250
skin0.2420.2410.6740.0930.2160.0550.2501.000

Missing values

2024-03-30T01:18:12.642237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T01:18:12.750344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

pregplaspresskininsumasspediage
318966239428.10.16721
40137403516843.12.28833
637850328831.00.24826
82197704554330.50.15853
131189602384630.10.39859
145166721917525.80.58751
160118844723045.80.55131
18110330388343.30.18333
19111570309634.60.52932
203126884123539.30.70427
pregplaspresskininsumasspediage
74413153883714040.61.17439
74512100843310530.00.48846
74718174415746.31.09632
7483187702220036.40.40836
751112178397439.00.26128
7530181884451043.30.22226
7551128883911036.51.05737
76028858261628.40.76622
76310101764818032.90.17163
7655121722311226.20.24530